Data Mining in CRM - Data Mining Techniques in CRM: Inside Customer Segmentation - page 14

and implementing successful data mining projects. We also outlined how data
mining can help an organization to better address the CRM objectives and achieve
''individualized'' and more effective customer management through customer
insight. The following list summarizes some of the most useful data mining
applications in the CRM framework:

• Customer segmentation:

- Value-based segmentation: Customer ranking and segmentation accord-
ing to current and expected/estimated customer value.
- Behavioral segmentation: Customer segmentation based on behavioral
attributes.
- Value-at-risk segmentation: Customer segmentation based on value and
estimated voluntary churn propensity scores.

• Targeted marketing campaigns:

- Voluntary churn modeling and estimation of the customer's likelihood/
propensity to churn.
- Estimation of the likelihood/propensity to take up an add-on product, to
switch to a more profitable product, or to increase usage of an existing
product.
- Estimation of the lifetime value (LTV) of customers.

Table 1.2 presents some of the most widely used data mining modeling
techniques together with an indicative listing of the marketing applications they
can support.

Table 1.2 Data mining modeling techniques and their applications.

Category of modeling Modeling techniques Applications

techniques

Classification (propensity)
models

Neural networks, decision
trees, logistic regres-
sion, etc.

• Voluntary churn pre-
diction
• Cross/up/deep selling

Clustering models K-means, TwoStep,

Kohonen network/self-
organizing map, etc.

• Segmentation

Association and sequence
models

A priori, Generalized Rule
Induction, sequence

• Market basket analysis
• Web path analysis